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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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In-situ Monitoring of Maximum and Average Chip Junction Temperature within Multichip IGBT Power Modules Using Kelvin Emitter Voltage

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Yang, Jianxiong Ran, Li Che, Yanbo Du, Mingxing Tianjin University of Technology Tianjin Key Laboratory of Control Theory and Applications in Complicated System Tianjin300384 China Chongqing University State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical Engineering Chongqing400044 China University of Warwick School of Engineering CoventryCV4 7AL United Kingdom Tianjin University Key Laboratory of Smart Grid of Ministry of Education School of Electrical and Information Engineering Tianjin300072 China

Parallel configurations of power chips within a multichip IGBT power module (M-IGBT-PM) are employed to meet the required current and power ratings. When the internal chips of an M-IGBT-PM experience uneven temperature distributions and package aging, it becomes challenging to determine the maximum and average temperatures of the chips within the module. This paper investigates the relationship between four transient thermal-sensitive electrical parameters (T-TSEPs) derived from the Kelvin emitter voltage and the chip junction temperature, while establishing relevant mathematical models. It compares the sensitivity and linearity of these four T-TSEPs, and also assesses the influence of external gate resistance, bus voltage, and load current on each of them. A key innovation in this paper is the proposal of a method for monitoring the maximum and average temperatures across the chips within the M-IGBT-PM, which is not influenced by the load current. The experimental results suggest that the temperature derived from the quasi-turn-on delay time is a good approximation of the maximum chip temperature, whereas the temperatures predicted by the other T-TSEPs are nearer to the average temperature. © 2025 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Giant Tunneling Magnetoresistance Based on Spin-Valley-Mismatched Ferromagnetic Metals

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Physical Review Letters 2025年第3期134卷 036302-036302页

作者： Kun Yan Li Cheng Yizhi Hu Junjie Gao Xiaolong Zou Xiaobin Chen School of Science State Key Laboratory on Tunable Laser Technology and Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen 518055 China Shenzhen Geim Graphene Center and Shenzhen Key Laboratory of Advanced Layered Materials for Value-added Applications Institute of Materials Research Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan 030006 China

Half metals, which are amenable to perfect spin filtering, can be utilized for high-magnetoresistive devices. However, available half metals are very limited. Here, we demonstrate that materials with intrinsic spin-valley-mismatched (SVM) states can be used to block charge transport, resembling half metals and leading to giant tunneling magnetoresistance. As an example, by using first-principles transport calculations, we show that ferromagnetic 1T−VSe2, 1T−VS2, and 2H−VS2 are such spin-valley-mismatched metals, and giant magnetoresistance of more than 99% can be realized in spin-valve van der Waals (vdW) junctions using these metals as electrodes. Owing to the intrinsic mismatch of spin states, the central-layer materials for the vdW junctions can be arbitrary nonmagnetic materials, in principle. Our research provides clear physical insights into the mechanism for high magnetoresistance and opens new avenues for the search and design of high-magnetoresistance devices.

关键词： Van der Waals forces

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THC-DL: Three-Party Homomorphic Computing Delegation Learning for Secure Optimization

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IEEE Internet of Things Journal 2025年

作者： Yang, Ze Tian, Youliang Xiong, Jinbo Niu, Kun Li, Mengqian Zhang, Xinyu Ma, Jianfeng Guizhou University Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology College of Computer Science and Technology Guiyang550025 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Xidian University State Key Laboratory of Integrated Services Network School of Cyber Engineering Xi’an710071 China

Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving machine learning (PPML) in delegation learning, yet faces critical challenges in functionality (non-linear activation support), practicality (ciphertext blow-up from iterative computations), and security (data leakage risks caused by public knowledge of the mathematical principles applied in model training). To tackle these challenges, we propose THC-DL, a three-party HE framework addressing these challenges holistically for the first time. We elaborately design the ciphertext secure comparison (DL-CSC) protocol to satisfy secure comparison with private inputs, enabling efficient non-linear operations with O(1) communication complexity that reduces runtime to 12.5% of the DGK (Dolev-Greensmith-Kent protocol, the well-known comparison protocol). Second, we construct a Truncation-Mapping (Tru-Map) scheme, a mechanism that transforms input data by truncating and mapping it into a domain that facilitates more efficient processing, and the addition of truncated mapped data to resolve ciphertext blow-up by adaptively scaling ciphertexts during iterative training, ensuring correctness. Third, we formalize data leakage risks in HE-based quadratic convex optimization (standard in ML) and apply THC-DL to construct a secure optimization scheme. Theoretical analysis confirms THC-DL’s resilience against input recovery attacks, even when adversaries exploit public model parameters. Experiments on a real-world platform validate DL-CSC’s efficiency and scalability while reducing the computational complexity and communication complexity from O(n) to O(1) where n denotes the length of the input bits. © 2014 IEEE.

关键词： Convex optimization

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Defense Against Adversarial Faces at the Source: Strengthened Faces Based on Hidden Disturbances

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Li, Shuangliang Wang, Jinwei Wu, Hao Zhang, Jiawei Cheng, Xin Luo, Xiangyang Ma, Bin School of Software Nanjing Universityof Information Science and Technology Nanjing210044 China College of Cryptology and Cyber Science Nankai University Tianjing300073 China State Key Laboratory of Mathemat-ical Engineering and Advanced Computing Zhengzhou450002 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Cyber Security Qilu University of Technology Shandong Academy of Sciences Shandong250353 China

Face Recognition (FR) systems, while widely used across various sectors, are vulnerable to adversarial attacks, particularly those based on deep neural networks. Despite existing efforts to enhance the robustness of FR models, they still face the risk of secondary adversarial attacks. To address this, we propose a novel approach employing "strengthened face" with preemptive defensive perturbations. Strengthened face ensures original recognition accuracy while safeguarding FR systems against secondary attacks. In the white-box scenario, the strengthened face utilizes gradient-based and optimization-based methods to minimize feature representation differences between face pairs. For the black-box scenario, we propose Shielded Gradient Sign Descent (SGSD) to optimize the gradient update direction of strengthened faces, ensuring the transferability and effectiveness against unknown adversarial attacks. Experimental results demonstrate the efficacy of strengthened faces in defending against adversarial faces without compromising the performance of FR models or face image visual quality. Moreover, SGSD outperforms conventional methods, achieving an average performance improvement of 4% in transferability across different attack intensities. © 2020 IEEE.

关键词： adversarial attacks face recognition facial protection

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Opposite long-term trends in precipitation during spring and summer over the Pamir Plateau and influential factors

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Atmospheric Research 2025年 323卷

作者： Chen, Ping Sun, Bo Wang, Huijun Yao, Junqiang State Key Laboratory of Climate System Prediction and Risk Management Key Laboratory of Meteorological Disaster Ministry of Education Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing210044 China School of Atmospheric Sciences Nanjing University of Information Science and Technology Nanjing210044 China Zhuhai China Institute of Desert Meteorology China Meteorological Administration Urumqi830002 China

As a vital part of the water tower of Asia, water cycle changes in the Pamir Plateau have significant impacts on the climate of the arid inland regions of Central Asia. Using the high-precision precipitation dataset known as TPHiPr, a significant wetting trend in spring and a drying trend in summer over the Pamir Plateau from 1979 to 2020 is identified in this study. Further analysis reveals that dynamic factors are more important than thermodynamic factors in driving these precipitation trends throughout both seasons. The dynamic factors are closely linked to the anticyclonic trend in spring and the cyclonic trend in summer, both of which are influenced by long-term trends in atmospheric circulation. Furthermore, the warming trend of sea surface temperature (SST) in the North Atlantic (NA) may impact atmospheric circulation over the Pamir Plateau. In spring, the pronounced meridional gradient of potential vorticity over the NA facilitates the northward propagation of the wave train induced by the warm SST along the great circle path, resulting in an anticyclonic trend over the Pamir Plateau. In summer, the wave train excited by the heat source at 60°N propagates eastward along the jet stream, leading to a cyclonic anomaly over the Pamir Plateau. The results were also verified in the Atlantic-pacemaker simulations. Overall, this study highlights the distinct wave trains induced by the NA SST warming trend in spring and summer. These distinct wave trains provide different dynamic conditions contributing to the observed drying and wetting trends over the Pamir Plateau. © 2025

关键词： Budget control

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and Networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined Networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive Networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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Bidirectional generative diffusion model with cascaded symmetric attention for brain connectivity-to-connectivity translation

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Biomedical Signal Processing and Control 2025年 108卷

作者： Zuo, Qiankun Di, Yi Ye, Conghuan Shi, Binghua Chen, Junyi Wei, Hui Li, Ruiheng Lei, Bangjun Hubei Key Laboratory of Digital Finance Innovation Hubei University of Economics Hubei Wuhan430205 China School of Information Engineering Hubei University of Economics Hubei Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China Hubei Internet Finance Information Engineering Technology Research Center Hubei University of Economics Hubei Wuhan430205 China

Understanding the complex relationship between structural and functional connectivity is fundamental to discovering the pathogenesis of brain diseases. However, it is challenging to establish nonlinear connective relationships between structural and functional modalities. In this study, a novel bidirectional generative diffusion model (BGDM) is proposed to construct bidirectional mapping for connectivity-to-connectivity translation. The proposed BGDM is an AIGC framework to learn bidirectional translation between structural and functional connectivity. By designing the cascaded symmetric attention module, the BGDM can learn multi-channel and cascaded edge features to estimate diffusion noise, which improves computational efficiency and enhances the model's ability to predict brain connectivity patterns. Furthermore, the connectivity noise-balanced loss is devised to adaptively adjust the importance of individual noisy connections and ensure more accurate and reliable translations between diverse brain connectivity representations. We demonstrate the effectiveness of BGDM through comprehensive experiments on the HCP datasets. Our model achieves the best translation performance with the mean MAE/SSIM of 0.065/0.835 for SC-to-FC translation, and the mean MAE/SSIM of 0.050/0.932 for FC-to-SC translation. Compared with the state-of-the-art method, the results of our model show improvements of 0.014 (MAE) and 0.06 (SSIM) for the SC-to-FC task, 0.014 (MAE) and 0.04 (SSIM) for FC-to-SC task. Our model can generate incomplete multimodal brain networks, which can be used to improve the accuracy of brain disease diagnosis and provide clinicians with disease-related biomarkers. Moreover, the proposed model has the potential to bridge the gap between structural connectivity and functional connectivity, offering new opportunities for understanding the brain's working mechanisms and revealing brain disease's pathogenesis. © 2025 Elsevier Ltd

关键词： Mapping

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Pulsating Torque Suppression of Flux Reversal Machines Based on Specific Harmonic Contributions of Rotor Permeance and PM MMF

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IEEE Transactions on Industry Applications 2025年

作者： Li, Junzhou Gao, Yuting Zhang, Chaofeng Zha, Xiaoming Qu, Ronghai Wuhan University School of Electrical Engineering and Automation Wuhan430072 China Key Laboratory of Integrated Energy Power Equipment and System Security Hubei430206 China State Grid Henan Electric Power Research Institute Zhengzhou450052 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Wuhan430074 China

Flux reversal machines (FRMs) exhibit high torque density, but also significant pulsating torque, including cogging torque and torque ripple. Traditional methods fail to separate the harmonics that affect torque density and pulsating torque, leading to a degradation of torque density when reducing pulsating torque. Therefore, to maintain torque density and simultaneously reduce pulsating torque, this paper proposes a novel FRM configuration incorporating shape designs for the rotor and permanent magnets (PMs). First, the key generation mechanisms of cogging torque, average torque, and torque ripple are thoroughly examined, with analytical expressions derived to classify the torque contributions of all air-gap magnetic field harmonics into negative-effect and positive-effect components. Further, the rotor and PM shapes are designed to enhance the positive-effect components and reduce the negative-effect ones of the air-gap permeance and magnetomotive force (MMF). The impacts of geometric parameters are evaluated by using a sensitivity-based hierarchical optimization approach. Then, the proposed FRM is comprehensively compared with a conventional FRM in terms of back-electromotive force (EMF), cogging torque, average torque, torque ripple, efficiency, etc. The results indicate that the proposed machine configuration achieves reductions in cogging torque and torque ripple by 90% and 86.8%, respectively, while maintaining consistent average torque. Finally, a 12-slot/10-pole FRM prototype was built and tested to validate the effectiveness of the proposed machine topology. © 1972-2012 IEEE.

关键词： Permanent magnets

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Mimicking the Familiar: Dynamic Command Generation for information Theft Attacks in LLM Tool-Learning system

arXiv

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arXiv 2025年

作者： Jiang, Ziyou Li, Mingyang Yang, Guowei Wang, Junjie Huang, Yuekai Chang, Zhiyuan Wang, Qing State Key Laboratory of Intelligent Game Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences China University of Queensland Australia

information theft attacks pose a significant risk to Large Language Model (LLM) tool-learning systems. Adversaries can inject malicious commands through compromised tools, manipulating LLMs to send sensitive information to these tools, which leads to potential privacy breaches. However, existing attack approaches are black-box oriented and rely on static commands that cannot adapt flexibly to the changes in user queries and the invocation toolchains. It makes malicious commands more likely to be detected by LLM and leads to attack failure. In this paper, we propose AUTOCMD, a dynamic attack command generation approach for information theft attacks in LLM tool-learning systems. Inspired by the concept of mimicking the familiar, AUTOCMD is capable of inferring the information utilized by upstream tools in the toolchain through learning on open-source systems and reinforcement with examples from the target systems, thereby generating more targeted commands for information theft. The evaluation results show that AUTOCMD outperforms the baselines with +13.2% ASRTheft, and can be generalized to new tool-learning systems to expose their information leakage risks. We also design four defense methods to effectively protect tool-learning systems from the attack. © 2025, CC BY.

关键词： information leakage

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